We could in theory use an external model of noise to account for these value discrepancies (and the ASR errors we model in the next section).

this would further decrease the probability, as some probability mass currently assigned to the heldout data would have to be reserved for the possibility of string renderings other than those we observe.


The observed words of each block are generated by repeatedly sampling classes from the block's distribution π, and for each sampled class z, a single word is sampled from the class-specific distribution over words φ z .

under the block HMM, a class z is sampled once from the transition distribution, and words are repeatedly sampled from φ z .


the occurrence of a topic in one segment makes it likely to appear in the next.

we wish to learn arbitrary transitions, both positive and negative, between the latent classes.


Numerous previous studies have considered distant or weak supervision from a single relational database as an alternative to manual supervision for information extraction (Hoffmann et al., 2011;Weld et al., 2009;Bellare and McCallum, 2007;Bunescu and Mooney, 2007;Mintz et al., 2009;.

to these systems, our distant supervision NED system provides a meta-algorithm for generating an NED system for any database and any entity type.


When informally examining the rules induced by our system, we found that CD rules are similar in spirit to those written by rule developers.

the induced CR rules are too fine-grained.


One would then union together the candidates from each of the two groups into two different views, e.g., Per-FirstLast and PerLastCommaFirst, and write filter rules at the higher level of these two views, e.g., "Remove PerLastCommaFirst spans that overlap with a PerFirstLast span".

our induction algorithm considers CR rules consisting of combinations of CD rules directly, leading to many semantically similar CR rules, each operating over small parts of a larger semantic group (see rule in Section 6.1).


Sentiment classification has become a hot research topic in NLP community and various kinds of classification methods have been proposed, such as unsupervised learning methods (Turney, 2002), supervised learning methods (Pang et al., 2002), semi-supervised learning methods (Wan, 2009;Li et al., 2010), and cross-domain classification methods (Blitzer et al., 2007;Li and Zong, 2008;He et al., 2011).

imbalanced sentiment classification is relatively new and there are only a few studies in the literature.


(2011b) focus on supervised learning for imbalanced sentiment classification and propose a clustering-based approach to improve traditional under-sampling approaches.

the improvement of the proposed clustering-based approach over under-sampling is very limited.


Their simulation experiment on text categorization confirms that selecting class-balanced samples is more important than traditional active selection strategies like uncertainty.

the proposed experiment is simulated and non real strategy is proposed to balance the class distribution of the selected samples.


They first select a set of uncertainty samples and then randomly select balanced samples from the uncertainty-sample set.

the classifier used for selecting balanced samples is the same as the one for supervising uncertainty, which makes the balance control unreliable (the selected uncertainty samples take very low confidences which are unreliable to correctly predict the class label for controlling the balance).


We propose the weakly supervised Multi-Experts Model (MEM) for analyzing the semantic orientation of opinions expressed in natural language reviews.

to most prior work, MEM predicts both opinion polarity and opinion strength at the level of individual sentences; such fine-grained analysis helps to understand better why users like or dislike the entity under review.


Note that the bigram features in x i partially capture sentence similarity.

such features cannot be extended to longer subsequences such as trigrams due to data sparsity: useful features become as infrequent as noisy terms.


Similar to SO-CAL, we determine the prior polarity of a phrase based on the BoO dictionary.

to SO-CAL, we directly use the BoO score as a feature because the BoO predictor weights have been trained on a very large corpus and are thus reliable.


Many researchers have developed several algorithms for this purpose and generated large static lexicons of words marked with prior polarities (Hatzivassiloglou and McKeown, 1997;Turney et al., 2003;Esuli, 2008;Mohammad et al., 2009;Velikovich et al., 2010).

there exist some polarity-ambiguous words, which can dynamically reflect different polarities along with different contexts.


Thus some previous work turned to investigate its surrounding contexts' polarities (such as the sentences before or after s), and then assigned the majority polarity to the collocation c (Hatzivassiloglou and McKeown, 1997;Hu and Liu, 2004;Kanayama and Nasukawa, 2006).

since the amount of contexts from the original review is very limited, the final resulting polarity for the collocation c is insufficient to be reliable.


Overall, most of the above approaches aim to generate a large static polarity word lexicon marked with prior polarities.

it is not sensible to predict a word's sentiment orientation without considering its context.


This suggests that the contexts expanded from other reviews are helpful in disambiguating the collocation's polarity.

exp dataset is just effective in disambiguating the polarity of such a collocation c, which appears many times in the domain-related reviews.


This can result in the low accuracy of Strategy0.

we can find that the other three query expansion strategies perform well.


As a matter of fact, the same holds true for the task of detecting paraphrases.

to RTE, this latter task requires bi-directional entailments, i.e., each of the two phrases must entail the other.


The advantage of our method compared to offthe-shelf clustering techniques is two-fold: On the one hand, the clustering algorithm is free of any parameters, such as the number of clusters or a clustering threshold, that require fine-tuning.

the approach makes use of a termination criterion that very well represents the nature of the goal of our task, namely to align pairs of predicates across comparable texts.


Hence, the extracted sentence pairs do not always contain gold alignments.

even sentence pairs that contain gold alignments are generally less parallel than in the previous settings, which make them harder to align.


The increased difficulty can also be seen in the results for the Greedy baseline, which only achieves an F 1 -score of 20.1% in this setting.

we observe that the majority of all sure alignments (60.3%) can be retrieved by applying the LemmaId model.


(2000), Lapata (2003) propose a probabilistic model for finding the correct interpretation of such metonymies in an unsupervised manner.

these event type metonymies differ from the problem dealt with in our paper and the SemEval 2007 task in that their recognition (i.e.


Learning inference relations between verbs is at the heart of many semantic applications.

most prior work on learning such rules focused on a rather narrow set of information sources: mainly distributional similarity, and to a lesser extent manually constructed verb co-occurrence patterns.


This led to more precise rule extraction, but with poor coverage since contrary to nouns, in which patterns are common (Hearst, 1992), verbs do not co-occur often within rigid patterns.

verbs do tend to co-occur in the same document, and also in different clauses of the same sentence.


Stative verb, such as 'love' and 'think', usually describe a state that lasts some time.

event verbs, such as 'run' and 'kiss', describe an action.


Adding latent variables to these models gives us additional modeling power and have shown success in applications like POS tagging (Merialdo, 1994), speech recognition (Rabiner, 1989) and object recognition (Quattoni et al., 2004).

this comes at the cost that the resulting parameter estimation problem becomes non-convex and techniques like EM (Dempster et al., 1977) which are used to estimate the parameters can only lead to locally optimal solutions.


(2006) and Musillo and Merlo (2008) have shown that learning PCFGs and dependency grammars respectively with latent variables can produce parsers with very good generalization performance.

both these approaches rely on EM for parameter estimation and can benefit from using spectral methods.


When m = 1, this conditioning is analogous to TNG's word distribution.

in contrast with TNG, the word Figure 3: Illustration of the hierarchical Pitman-Yor process for a toy two-word vocabulary V = {honda, civic} and two-topic (T = 2) model with m = 1.


As such most prior work for learning SCFGs has relied on inference algorithms that were heuristically constrained or biased by word-based alignment models and small experiments (Wu, 1997;Zhang et al., 2008;Blunsom et al., 2009;Neubig et al., 2011).

to these previous attempts, our SCFG model scales to large datasets (over 1.3M sentence pairs) without imposing restrictions on the form of the grammar rules or otherwise constraining the set of learnable rules (e.g., with a word alignment).


A naive instantiation of this strategy is to visit all |s| 2 |t| 2 bispans in some order.

since we wish to be able to draw many samples, this is not computationally feasible.


A much more efficient approach avoids resampling variables that would result in violations without visiting each of them individually.

to ensure detailed balanced is maintained, the order that we resample bispans has to match the order we would sample them using any exhaustive approach.


It works particularly well in the sentence extraction paradigm.

additional elements are known to be good predictors of important information.


Assuming that their parent head is the main verb of the sentence, a longshort sequence would minimize overall dependency length.

in 613 examples found in the Penn Treebank, the average length of the first adjunct was 3.15 words while the second adjunct was 3.48 words long, thus reflecting a short-long pattern, as illustrated in the Temperley p.c.


Consequently, the score function, which denotes some criterion for the quality of a summary, tends to be determined so that the function can be decomposed to components and it is solved with global inference algorithms, such as ILP.

both decomposing the score function properly and utilizing the evaluation of half-way process of searches are generally difficult.


As insert i actions are dominant in the extractive approach, the execution time increases linearly with respect to the number of textual units.

iLP has to take into account the combinations of textual units, whose number increases exponentially.


For example, in one sentence, the probability of co-occurrence of "present → present", "past → past" and "future → present" is more than other combinations, which can be against tense inconsistency errors described in Observation (1) and (2) (see Section 1).

it seems strange that "present → past" exceeds "present → future".


Compared to IN2EN, all CN:* models have higher 2/3/4-gram precision.

cN:pivot has lower unigram precision, which could be due to bad word alignments, as the results for cN:pivot ′ show.


Table 6 shows that CN:pivot,t=0 is better/equal to the original 53%/31% of the time.

cN:pivot ′ +morph is typically worse than the original; even compared to the best in top 3, the better:worse ratio is 45%:43%.


We believe this is because lattice encodes many options, but does not use a Malay LM, while 1-best uses a Malay LM, but has to commit to 1-best.

cN:pivot uses both n-best outputs and an Indonesian LM; designing a similar setup for reversed adaptation is a research direction we would like to pursue in future work.


It is undeniable that a huge progress has been reached in the field of supervised dependency parsing, especially due to the CoNLL shared task series.

when it comes to unsupervised parsing, there are surprisingly few clues we could rely on.


(2011) showed that such projection produce better structures than the current unsupervised parsers do.


Other way that would lead to lower sparsity would be searching for sequences of part-of-speech tags instead of sequences of word forms.

this also does not bring desired results.


While the first method generates only flat trees, the second one can generate all possible projective trees.

the sampler converges to similar results for both the initializations.


Our main motivation for developing an unsupervised dependency parser was that we wanted to be able to parse any language.

the experiments show that our parser fails for some languages.


Graph-based parsers (Eisner, 1996;McDonald et al., 2005) are based on global optimization of models that work by scoring subtrees.

transition-based parsers (Yamada and Matsumoto, 2003;Nivre et al., 2004), which are the focus of this work, use local training to make greedy decisions that deterministically select the next parser state.


In that paper, the LEFT-ARC transition from Nivre's arc-eager transition system is added to a list-based parser.

the goal of that transition is different from ours (selecting between projective and nonprojective parsing, rather than building some arcs in advance) and the approach is specific to one algorithm while ours is generic -for example, the LEFT-ARC transition cannot be added to the arc-standard and arc-eager parsers, or to extensions of those like the ones by Attardi (2006) or Nivre (2009), because these already have it.


Non-projective transitions that create dependency arcs between non-contiguous nodes have been used in the transition-based parser by Attardi (2006).

the transitions in that parser do not use the second buffer node, since they are not intended to create some arcs in advance.


Each of these higher-order parsing algorithms makes a clever factorization for the specific model in consideration to keep complexity as low as possible.


Thus, the addition of new higher-order features, including valency, extra thirdorder, and label tuple features, results in increased accuracy.

this is not without cost as the run-time in terms of tokens/sec decreases (300 to 220).


For example, /m/ is a nasal (air flowing through the nostrils), while /p/ is a plosive (obstructed air suddenly released through the mouth).

vowels are voiced sounds produced with an open vocal tract.


This result is somewhat surprising, as we expected these features to be quite informative.

it appears that whatever information they convey is redundant when considering the text-based feature sets.


First, we notice that the average entropy of voiced vs. unvoiced consonants is nearly identical in both cases, close to the optimal value.

when we examine the dimensions of place and manner, we notice that the entropy induced by our model is not as high as that of the true consonant inventories, implying a suboptimal allocation of consonants.


Collecting all such links within English Wikipedia yields a large number of aliases for each page.

many redirects are for topics other than individual people, and these would be poor examples of name variation.


Our method is really intended to be run on a corpus of string tokens.

for experimental purposes, we instead use the above dataset of string types because this allows us to use the "ground truth" given by the Wikipedia redirects.


The other way around (rejecting what should be allowed) is easier to check, and we find that of 13K word types in AMI, about 7.2% are rejected for non-appearance in Gigaword, after filtering for interjections like "mm-hmm".

we manually checked them and returned all but 2.9% of word types to the corpus.


Our result points a way towards a direction for explaining of this phenomenon by demonstrating that the differences between currenttechnology artificial speech and natural speech can be partially explained through higher-level syntactic features.

further experimentation is required on other measures of syntactic complexity (e.g.


This setprovided by Yahoo Japan Corporation and contains 16 million questions asked from April, 2004to April 2009 ting may not necessarily reflect a "real world" distribution of why-questions, in which ideally a wide range of people ask questions that may or may not have an answer in our corpus.

qS3 allows us to evaluate our method under the idealized conditions where we have a perfect answer retrieval module whose answer candidates always contain at least one correct answer (the source passage used for creating the why-question).


(2007) used lexical-conceptual templates for query generation.

this work did not address the crucial issue of disambiguating the constituents of the question.


In statistical machine translation, minimum error rate training (MERT) is a standard method for tuning a single weight with regard to a given development data.

due to the diversity and uneven distribution of source sentences, there are two problems suffered by this method.


Since the transformed classification problem is not linearly separable, there does not exist a single weight which can obtain e 11 and e 21 as translation results meanwhile.

one can obtain e 11 and e 21 with weights: 1, 1 and −1, 1 , respectively.


Therefore, there exists no single weight W which simultaneously obtains e 11 and e 21 as translation for f 1 and f 2 via Equation (1).

we can achieve this with two weights: 1, 1 for f 1 and −1, 1 for f 2 .


This method can automatically adapt the divergence between different annotation guidelines and bring improvement to Chi-nese word segmentation.

the need of cascaded classification decisions makes it less practical for tasks of high computational complexity such as parsing, and less efficient to incorporate more than two annotated corpora.


In decoding, a raw sentence is first decoded by the source classifier, and then inputted into the transformation classifier together with the annotations given by the source classifier, so as to obtain an improved classification result.

annotation adaptation has a drawback, it has to cascade two classifiers in decoding to integrate the knowledge in two corpora, thus seriously degrades the processing speed.


Considering the fact that today some corpora for word segmentation are really large (usually tens of thousands of sentences), it is necessary to obtain the latest CTB and investigate whether and how much does annotation transformation bring improvement to a much higher baseline.

it is valuable to conduct experiments with more source-annotated training data, such as the SIGHAN dataset, to investigate the trend of improvement along with the increment of the additional annotated sentences.


They approach lexical variant detection by using a context fitness classifier (Han and Baldwin, 2011) or through dictionary lookup (Gouws et al., 2011).

the lexical variant detection of both methods is rather unreliable, indicating the challenge of this aspect of normalisation.


Our method adopts a similar strategy using distributional/string similarity, but instead of constructing a small lexicon for preprocessing, we build a much wider-coverage normalisation dictionary and opt for a fully lexiconbased end-to-end normalisation approach.

to the normalisation dictionaries of Han and Baldwin (2011) and Gouws et al.


considering that S-dict is an automatically-constructed dictionary targeting lexical variants of varying frequency, it is not surprising that the precision is worse than that of HB-dictwhich is manually-constructed -and GHM-dictwhich includes entries only for more-frequent OOVs for which distributional similarity is more accurate.


Their approach works well when each sentence potentially refers to one of a small set of possible meanings, such as in the sportscasting task.

it does not scale to problems with a large set of potential meanings for each sentence, such as the navigation instruction following task studied by Chen and Mooney (2011).


assume that every atomic MR generates at least one NL word.

since we do not know which subgraph of the overall context (i.e.


(2011) simply read the MR, m, for a sentence off the top S m nonterminal of the most probable parse tree.

in our approach, the correct MR is constructed by properly composing the appropriate subset of lexeme MRs from the most-probable parse tree.


(2010) developed a system that learns to map NL instructions to executable commands for a robot navigating in an environment constructed by a laser range finder.

their approach has limitations of ignoring any objects or other landmarks in the environment to which the instructions can refer.


Most of today's SMT systems depends heavily on parallel corpora aligned at the word-level to train their different component models.

such annotations do have their drawbacks in training.


from free text, has received renewed interest in the "big data" era, when petabytes of natural-language text containing thousands of different structure types are readily available.

traditional supervised methods are unlikely to scale in this context, as training data is either limited or nonexistent for most of these structures.


Since then, the approach grew in popularity (Bunescu and Mooney, 2007;Bellare and McCallum, 2007;Wu and Weld, 2007;Mintz et al., 2009;Riedel et al., 2010;Hoffmann et al., 2011;Nguyen and Moschitti, 2011;Sun et al., 2011;Surdeanu et al., 2011a).

most of these approaches make one or more approximations in learning.


Thus the log-likelihood for this problem is not convex (it includes a sum of products).

we can still use EM, but the optimization focuses on maximizing the lower bound of the loglikelihood, i.e., we maximize the above joint probability for each entity pair in the database.


It focused on higher textual dimensions, such as inference load (Kintsch and Vipond, 1979;Kemper, 1983), density of concepts (Kintsch and Vipond, 1979), or macrostructure (Meyer, 1982).

these attempts did not achieve better results than the classic approach, even though they used more principled and more complex features.


Since our study intended to design a generic model, we focused on specific predictors affecting L2 reading, whatever the learner's mother tongue is: Multi-word expressions (MWE): MWEs are acknowledged to cause problems to L2 learners for production (Bahns and Eldaw, 1993).

the effect of MWE on the reception side remains unclear, especially for beginners.


First, we showed that maximizing the type of linguistic information might not be the best path to go, since a model based on four lexico-syntactic features yielded predictions as accurate as those of a model relying on our Exp1 set of variables.

this finding might be partly accounted by the lower predictive power of the features from the semantic and specific-to-FFL family, with the notable exception of the LSA-based predictor (avLocalLsa-Lem), which is the third best predictor when considered alone.


Not only the expert models, to which we imposed the presence of one or two semantic predictors, did not perform the best, but none of the features from our semantic set was retained during the automatic selection of the variables for the logistic models.

in some subsets, the LSA-based feature was sometimes considered as collinear with the other variables.


In case (c), it is possible for there to be multiple children that inherit this newly created gap if multiple children had descendents on both sides.

the assumption of upward movement in the phrase structure tree should rule out movement into the projection interval of a non-ancestor.


In (1), the coreference relation between One of the key suspected Mafia bosses arrested yesterday and Lo Presti can be found by knowing that their predicates (i.e., has hanged and had hanged) corefer.

the coreference relations between the arguments Saints and Bush in (2) helps to determine the coreference relation between their predicates placed and put.


Our model follows a cautious (or "baby steps") approach, which we previously showed to be successful for entity coreference resolution (Raghunathan et al., 2010;Lee et al., 2011).

unlike our previous work, which used deterministic rules, in this paper we learn a coreference resolution model using linear regression.


Note that this avoids inspecting many of the possible cluster combinations: once a cluster is built (e.g., during the previous iterations or by the deterministic sieves in step 8), we do not generate training data from its members, but rather treat it as an atomic unit.

our approach generates more training data than online learning, which trains using only the actual decisions taken during inference in each iteration (i.e., the pair (e 1 , e 2 ) in step 13).


The performances when the phrase length is 1 are better than that of single word-based TM (row 6 and row 13 in Table 2), this suspect that the features in our linear ranking model are useful.

it will be instructive to explore the methods of preserving the improvement generated by longer phrase when more features are incorporated in the future work.


While traditional Information Extraction (IE) (ARPA, 1991;ARPA, 1998) focused on identifying and extracting specific relations of interest, there has been great interest in scaling IE to a broader set of relations and to far larger corpora (Banko et al., 2007;Hoffmann et al., 2010;Mintz et al., 2009;Carlson et al., 2010;.

the requirement of having pre-specified relations of interest is a significant obstacle.


To apply pattern #1 from Figure  3 we first match arg1 to 'festival', rel to 'scheduled' and arg2 to '25th' with prep 'for'.

(festival, be scheduled for, 25th) is not a very meaningful extraction.


The rest are due to other causes such as OLLIE's ability to handle relationships mediated by nouns and adjectives, or REVERB's shallow syntactic analysis, etc.

oLLIE misses very few extractions returned by REVERB, mostly due to parser errors.


It also indicates if it contributes a new table, or a count to t i,j,k for the PDP at this node.

as we discussed above, this then contributes to either t i,j,k or s i,j,k .


Figure 8 shows similar topic evolution plots for LDA, STM and AdaTM.

the AdaTM topic evolutions are much clearer for the less frequent topics, as shown in Figure 8(c).


To some extent, our model is similar to Semi-Markov Conditional Random Fields (called a Semi-CRF), in which the segmentation and labeling can also be done directly (Sarawagi and Cohen, 2004).

semi-CRF just models label dependency, and it cannot capture more correlations between adjacent chunks, as is done in our approach.


the constant in the O is relatively large.


Otherwise, we use a simulated annealing strategy where hypotheses with a lower score can still be accepted with a certain probability which depends on the difference between the hypothesis score and Figure 1: Example of a search tree produced by the beam-search decoder for the input In other hands, they might be right.

the highest scoring hypothesis found is they might be right.


This study presents a novel method that measures English language learners' syntactic competence towards improving automated speech scoring systems.

to most previous studies which focus on the length of production units such as the mean length of clauses, we focused on capturing the differences in the distribution of morpho-syntactic features or grammatical expressions across proficiency.


An assessment of ESL learners' syntactic competence should consider the structure of sentences as a whole -a task which may not be captured by the simplistic POS tag distribution.

studies of Lu (2010) and Chen and Zechner (2011) showed that more complex syntactic features are unreliable in ASR-based scoring system.


Excitation values are determined by spreading activation applied to the network, given a small number of manually prepared seed templates.

we cannot construct the network unless we know whether each noun pair is PNP or NNP, due to the configuration of the constraint matrix, and currently we have no feasible method to classify all of them into PNPs and NNPs in advance.


For example, the pair emit smells ⊥ reduce smells is not logically contradictory since the two events can happen at the same time.

they become almost contradictory when their tendencies get stronger (i.e., emit smells more strongly ⊥ thoroughly reduce smells).


(1) a. look like ⇔ resemble b. control system ⇔ controller The challenge in acquiring paraphrases is to ensure good coverage of the targeted classes of paraphrases along with a low proportion of incorrect pairs.

no matter what type of resource has been used, it has proven difficult to acquire paraphrase pairs with both high recall and high precision.


In terms of coverage, P Hvst is expected to be greatly larger than P Seed , although it will not cover totally different pairs of paraphrases, such as those shown in (1).

the quality of P Hvst depends on that of P Seed .


Thus, as long as at least one of them has a probability higher than the given threshold value, corresponding novel paraphrases can be harvested.

as a results of assessing each individual paraphrase pair by the contextual similarity, many pairs in P Hvst , which are supposed to be incorrect instances of their corresponding pattern, are filtered out by a larger threshold value for th s .


On the other hand, as a results of assessing each individual paraphrase pair by the contextual similarity, many pairs in P Hvst , which are supposed to be incorrect instances of their corresponding pattern, are filtered out by a larger threshold value for th s .

many pairs in P Seed have a relatively high similarity, e.g., 40% of all pairs have similarity higher than 0.4.


To obtain a better κ value, the criteria for grading will need to be improved.

we think that was not too low either 8 .


Several computational models have also investigated this interaction by adding manually annotated part-of-speech tags as input to word learning algorithms, and suggesting that integration of lexical categories can boost the performance of a cross-situational model (Yu 2006, Alishahi and.

none of the existing experimental or computational studies have examined the acquisition of word meanings and lexical categories in parallel.


explicit accounts of how such categories can be integrated in a crosssituational model of word learning have been rare.


The model of Niyogi (2002) simulates the mutual bootstrapping effects of syntactic and semantic knowledge in verb learning, that is the use of syntax to aid in inducing the semantics of a verb, and the use of semantics to narrow down possible syntactic frames in which a verb can participate.

this model relies on manually assigned priors for associations between syntactic and semantic features, and is tested on a toy language with very limited vocabulary and a constrained syntax.


verbs and nouns) can indeed help word learning in a more naturalistic incremental setting.

the model of Alishahi and Fazly (2010) integrates manually annotated part-ofspeech tags into an incremental word learning algorithm, and shows that these tags boost the over-all word learning performance, especially for infrequent words.


The weight of edges between different nodes is also measured by document similarity.

there are no edges between nodes and their initial sentiments because RANK is an iterative algorithm and each iteration gives new scores to unlabeled nodes while labeled nodes remain constant.


The relation between them is no relation, since it is unclear which occurs first.

e 5 and e 3 both happen in the interval I 2 but they form an overlap relation.


In these challenges, several temporal-related tasks were defined including the tasks of identifying the temporal relation between an event mention and a temporal expression in the same sentence, and recognizing temporal relations of pairs of event mentions in adjacent sentences.

with several restrictions imposed to these tasks, the developed systems were not practical.


Some of these predictors, including the identity -or even number (McClosky, 2008) -of alreadygenerated siblings, can be prohibitively expensive in sentences above a short length k. For example, they break certain modularity constraints imposed by the charts used in O(k 3 )-optimized algorithms (Paskin, 2001a;Eisner, 2000).

in bottom-up parsing and training from text, everything about the yield -i.e., the ordered sequence of all already-generated descendants, on the side of the head that is in the process of spawning off an additional child -is not only known but also readily accessible.


(In fact, 45% of all head-percolated dependencies in WSJ are between adjacent words.)

some common constructions are more remote: e.g., subordinating conjunctions are, on average, 4.8 tokens away from their dependent modal verbs.


As we are using the same data set as per the previous approach, we perform 5-fold cross validation as well.

the training for each fold is conducted with a different grammar consisting of only the vocabulary that occur in each training fold.


The work of Ahmed and Xing (2010) generalizes DTMs to iDTMs (infinite DTMs) by allowing topics to span only a subset of time slices, and allowing an arbitrary number of topics.

iDTMs still require placing documents into discrete epochs, and the issue of generating topic rather than document threads remains.


Metro maps are effectively sets of non-chronological threads that are encouraged to intersect and thus create a "map" of events and topics.

these approaches assume some prior knowledge about content.


For the datasets we use later, the actual number is around 2 1000 .

we will show how to construct the desired model in a way that allows efficient inference, even for large datasets, using determinantal point processes (DPPs).


It can equally be shown that the same holds for the constraints OneGP and NoGP.

when working with LP relaxations, the two polytopes have different fractional vertices.


Our work is most similar in spirit to the relaxation method presented by Riedel and Smith (2010) that incrementally adds second order edges to a graphical model based on a gain measure-the analog of our reduced cost.

they always score every higher order edge, and also provide no certificates of optimality.


Previously, domain adaptation learning has been successfully used in other NLP tasks such as relation extraction (Jiang, 2009) and POS tagging (Jiang and Zhai, 2007), semantic detection (Tan et al., 2008), name entity recognition (Guo et al., 2009) and entity type classification (Jiang and Zhai, 2007).

to the best of our knowledge, it has yet to be explored for coreference resolution.


Some of them have been success-fully used in coreference resolution (Pang and Fan, 2009;Munson et al., 2005;Rahman and Ng, 2011a).

these methods only focus on the withindomain setting.


With this feature set, we found that the linear kernel is insufficient to fit the training data.

using an rbf kernel would be too computationally expensive.


This is common among all ensemble methods.

the costs in (4) and 9are trivial as both are at the document level.


It is also possible to self-train a semantic parser without any labeled data (Goldwasser et al., 2011).

this approach does not perform as well as more supervised approaches, since the parser's self-training predictions are not constrained by the correct logical form.


If a source language L v is a resource-rich language, then the language model P (v I 1 ) can be well estimated from sufficient training texts.

if the source lan-guage L v is a resource-poor language, then the language model P (v I 1 ) cannot be reliably or robustly estimated due to lack of training texts.


Both ITG constraints and other constraints assume that all permutations are equally probable.

it makes sense to restrict those non-monotonic reorderings when performing the translation.


employ queries of the form "X of Y", where X and Y would be replaced with the wheel and the car, respectively.

we are not targeting a particular type of relation.


Specifically, if X is a verb, then it resolves the target pronoun to the candidate antecedent that has the same grammatical role as the pronoun.

if X is an adjective and the sentence does not involve comparison, then it resolves the target pronoun to the candidate antecedent serving as the subject of V .


had the triple occurred less than 100 times, both of these features would have been set to zero.


Hence, if the Stanford resolver decides to resolve the target pronoun, it will resolve it to one of the two candidate antecedents.

if it does not have enough confidence about resolving it, it will leave it unresolved.


Given that the Random baseline correctly resolves 50% of pronouns and the Stanford resolver correctly resolves only 40.1% of the pronouns, it is tempting to conclude that Stanford does not perform as well as Random.

recall that Stanford leaves 30.1% of the pronouns unresolved.


Narrative chains, on the other hand, are useful for capturing the relationship between the events described in the two clauses.

they are computed over verbs, and therefore cannot capture such a relationship when one or both of the events involved are not described by verbs.


For example, in Figure 3 (b), although label "root" is a parent of label "Computers & Internet", the topical words of label "Computers & Internet" show the topical node is not a child of label "root".

in Figure 3 (a), label "root" and "Computers & Internet" has corresponding parent-child relation between their topical words.


We can also obtain similar experimental results over Y Ans and O Hlth.

for the same reason of limitation of space, their detailed descriptions are skipped in this paper.


This is not surprising given the independent predictions of the model and the very general, language universal assumptions we have made in the model structure and feature sets.

in terms of gauging the usefulness of the hidden syntactic marginalization method the results are extremely compelling, with only marginal differences between the performance of the observed-syntax model, especially relative to the baseline.


The highest accuracy achieved to date under these assumptions is 91.6% (Ravi et al., 2010).

as is often noted (including by the authors themselves), many papers that work on learning taggers from tag dictionaries make unrealistic assumptions about the tag dictionaries they use as input (Toutanova and Johnson, 2008;Ravi and Knight, 2009;Hasan and Ng, 2009).


In the Greedy Set Cover phase this means choosing the tag bigram that would cover the most new tokens, and in the Greedy Path Completion phase this means choosing the tag bigram that would fill the most holes.

it is frequently the case that there are many distinct tag bigrams that would cover the most new tokens or fill the most holes, leaving the MIN-GREEDY algorithm with no choice but to randomly select from these options.


As was described above, the output of MIN-GREEDY's second stage is a minimized set of tag bigrams which is used as a constraint on the first iteration of the third stage, Iterative Model-Fitting.

in order to determine when to stop adding new bigrams during the first two phases, the MIN-GREEDY algorithm must try to find complete tag paths through each sentence in the raw corpus, stopping once a tag path has been found for each one.


This indicates that if the tag dictionary has a low degree of ambiguity, then MIN-GREEDY can make the situation worse.

with our smoothing techniques, we regain similar improvements as with the HMM.


This motivates an intensive study in automatically resolving person name ambiguity in various web applications.

resolving web person name ambiguity is not a trivial task.


Intuitively, each cluster will be treated as a topic.

we found that hub nodes usually correspond to general concepts which may be related to many topics, but with a loose relatedness.


we found that hub nodes usually correspond to general concepts, e.g., education or public, which may be related to many topics, but with a loose relatedness.


Intuitively, larger threshold can prune more unimportant edges and improve the disambiguation performance.

if the threshold is too large, we may prune important edges and harm the results.


First, DeNero and Uszkoreit (2011) learn a reordering model through a three-step process of bilingual grammar induction, training a monolingual parser to reproduce the induced trees, and training a reordering model that selects a reordering based on this parse structure.

our method trains the model in a single step, treating the parse structure as a latent variable in a discriminative reordering model.


The above features φ and their corresponding weights w are all that are needed to calculate scores of derivation trees at test time.

during training, it is also necessary to find model parses according to the loss-augmented scoring function S(D|F, w)+L(D|F, A) or oracle parses according to the loss L(D|F, A).


lader is able to improve over the orig baseline in all cases, but when equal numbers of manual and automatic alignments are used, the reorderer trained on manual alignments is significantly better.

as the number of automatic alignments is increased, accuracy improves, approaching that of the system trained on a smaller number of manual alignments.


The former matches most linguistic theories while the latter does not, but to a monolingual parser, these conventions are equally learnable.

once bilingual data is involved, such treebank conventions entail constraints on rule extraction that may not be borne out by semantic alignments.


The lowest VP in this tree is headed by 'select,' which aligns to the Chinese verb ' 挑选.'

' 挑 选' also aligns to the other half of the English infinitive, 'to,' which, following common English linguistic theory, is outside the VP.


Each classification decision is made independently, allowing for inconsistency at multiple levels (within a fluent, across fluents, or across entities).

using joint inference, the classifier component can determine the best overall span for each fluent.


To learn the model parameters, we start by using maximum-likelihood estimation for these multinomials from training entities.

some smoothing is required since new entities may contain previously unseen answers to existing questions.


Notice that there is usually only one headquarters at any point in time, although the location of a headquarters can change.

the relation arg1 is funded by arg2 is a non-unique relation since it is likely that there exist more than one funder.


Previous studies on discourse analysis have been quite successful in identifying what machine learning approaches and what features are more useful for automatic discourse segmentation and parsing (Soricut and Marcu, 2003;Subba and Eugenio, 2009;du-Verle and Prendinger, 2009).

all the proposed solutions suffer from at least one of the following two key limitations: first, they make strong independence assumptions on the structure and the labels of the resulting DT, and typically model the construction of the DT and the labeling of the relations separately; second, they apply a greedy, suboptimal algorithm to build the structure of the DT.


They use a very large set of features in their parser.

taking a radically-greedy approach, they model structure and relations separately, and ignore the sequence dependencies in their models.


The cell [i, j] in the DPT represents the span containing EDUs i through j and stores the probability of a constituent R[i, m, j], where m = argmax i≤k≤j P (R[i, k, j]).

to HILDA which implements a greedy algorithm, our approach finds a DT that is globally optimal.


Read as a whole, it is clear that the two texts describe the same three events, in the same order, and thus, e.g., 1.2 and 2.2 are paraphrases.

they share very few n-grams, nor named entities.


Intuitively we expected the MSA-based systems to end up with a higher recall than the clustering baselines, because sentences can be matched even if their similarity is moderate or low, but their discourse context is highly similar.

this is only the case for the system using BLEU scores, but not for the system based on the vector space model.


We mainly compute precision for this task, as the recall of paraphrase fragments is difficult to define.

we do include a measure we call productivity to indicate the algorithm's completeness.


It is what federal support should try to achieve  (Hajič et al., 2009).

the approach can only generate projective word orders (which can be drawn without any crossing edges).


In our case, it could potentially be beneficial for both the lifting classifier, and for the linearizer.

we found that marking liftings at best gave similar results as not marking, so we kept the original labels without marking.


Whether gold standard part-of-speech tags or distributional categories are better suited to applications like parsing or machine translation can be best decided using extrinsic evaluation.

in this study we follow previous work and evaluate our results by comparing them to gold standard part-of-speech tags.


Conversely, both Non Negative Matrix factorization and Latent Dirichlet Allocation learn concise and coherent topics and achieved similar performance on our evaluations.

nMF learns more incoherent topics than LDA and SVD.


Phrase-based machine translation models have shown to yield better translations than Word-based models, since phrase pairs encode the contextual information that is needed for a more accurate translation.

many phrase pairs do not encode any relevant context, which means that the translation event encoded in that phrase pair is led by smaller translation events that are independent from each other, and can be found on smaller phrase pairs, with little or no loss in translation accuracy.


Consequently, the lexical translation entry for Word-based models splits the probabilistic mass between different translations, leaving the choice based on context to the language model.

in Phrase-based Models, we would have a phrase pair p(in the box, dentro da caixa) and p(in china, na china), where the words "in the box" and "in China" can be translated together to "dentro da caixa" and "na China", which substantially reduces the ambiguity.


In this case, both the translation and language models contribute to find the best translation based on the local context, which generally leads to better translations.

not all words add the same amount of contextual information.


This is generally not desired, since the 2 smaller phrase pairs can be used to translate the same source sentence with a small probabil-ity loss (5%), even if the longer phrase is pruned.

if the smaller phrases are pruned, the longer phrase can not be used to translate smaller chunks, such as "the key in Portugal".


The example above shows an extreme case, where the event encoded in the phrase pair p(John in, John em) is decomposed into independent events, and can be removed without changing the model's prediction.

finding and pruning phrase pairs that are independent, based on smaller events is impractical, since most translation events are not strictly independent.


Ideally, we would want to minimize the relative entropy for all possible source and target sentences, rather than all phrases in our model.

minimizing such an objective function would be intractable due to reordering, since the probability assigned to a phrase pair in a sentence pair by each model would depend on the positioning of all other phrase pairs used in the sentence.


In the example above, where s="John in Portugal" and t="John em Portugal", the decoder would choose the derivation with the highest probability from s to t. Using the unpruned model, the possible derivations are either using phrase p(s, t) or one element of its support set S 1 , S 2 or S 3 .

on the pruned model where p(s, t) does not exist, only S 1 , S 2 and S 3 can be used.


One possible solution to address this problem is to perform pruning iteratively, from the smallest phrase pairs (number of words) and increase the size at each iteration.

we find this undesirable, since the model will be biased into removing smaller phrase pairs, which are generally more useful, since they can be used in multiple derivation to replace larger phrase pairs.


In theory, the multinomial distribution should yield better results, since the pruning model will prefer to prune phrase pairs that are more likely to be observed.

longer phrase pairs, which tend compete with other long phrase pairs on which get pruned first.


Given the shorter phrases from the table, the probability would be 0.7189 • 0.4106 • 0.0046 = 0.0014 * , which is about an order of a magnitude smaller than the original probability of 0.0128.

composing the phrase the French government out of shorter phrases has probability 0.7189 • 0.4106 • 0.6440 = 0.1901, which is very close to the original probability of 0.1686.


The results in Figure 2 to Figure 5 show that entropy-based pruning clearly outperforms the alternative pruning methods.

it is a bit hard to see from the graphs exactly how much additional savings it offers over other methods.


We can see that the pPDA extension gave modest improvements on the Urdu test set, but at a small decrease in performance on the Arabic data.

for Chinese, there is a substantial gain, particularly with jump distances of five or longer.


For example, all else equal, larger metric gains will tend to be more significant.

what does this relationship look like and how reliable is it?


Naively, it might seem like we would then check how often A beats B by more than δ(x) on x (i) .

there's something seriously wrong with these x (i) as far as the null hypothesis is concerned: the x (i) were sampled from x, and so their average δ(x (i) ) won't be zero like the null hypothesis demands; the average will instead be around δ(x).


Do and Roth (2010) use a KB (YAGO) to aid the generation of features from free text.

their method is designed specifically for extracting hierarchical taxonomic structures, while our algorithm can be used to discover relations for general general graph-based KBs.


The past decade has seen some promising solutions, unsupervised relation extraction (URE) algorithms that extract relations from a corpus without knowing the relations in advance.

most algorithms (Hasegawa et al., 2004, Shinyama and Sekine, 2006, Chen et.


Recently, Kok and Domingos (2008) proposed Semantic Network Extractor (SNE), which generates argument semantic classes and sets of synonymous relation phrases at the same time, thus avoiding the requirement of tagging relation arguments of predefined types.

sNE has 2 limitations: 1) Following previous URE algorithms, it only uses features from the set of input relation instances for clustering.


For example, SNE placed relation instances <Barbara, grow up in, Santa Fe> and <John, be raised mostly in, Santa Barbara> into 2 different clusters because the arguments and phrases do not share features nor could be grouped by SNE's mutual clustering.


follow (ii) -we arrive at 'subtree ranking by MaxEnt'.

the 'n-best training' uses global trees and Maximum Entropy for training, so the reason of the difference between 'perceptron with global training' and 'n-best training' is (ii).


This decomposability assumption provides a fine framework in the case of binary features which fire if a certain linguistic phenomenon occurs.

this is not straightforward in the presence of real valued features.


The QUERY procedure for CM-CU is identical to Count-Min.

to UPDATE an item "x" with frequency c, first we compute the frequencyĉ(x) of this item from the existing data structure: ) and the counts are updated according to: The intuition is that, since the point query returns the minimum of all the d values, we will update a counter only if it is necessary as indicated by the above equation.


In PLEB generating random permutations and sorting the bit vectors of size k involves worse preprocessing time than using IRP.

spending more time in pre-processing leads to finding better approximate nearest neighbors.


If we move down the table, with b = 100, IRP, PLEB, and FAST-PLEB get results comparable to LSH (reaches an upper bound).

using large b implies generating a long potential nearest neighbor list close to the size of the unique context vectors.


Overall the annotator found almost all the learned words to be similar to the query words.

the algorithm can not differentiate between different senses of the word.


This rule tests the tendency of two tokens appearing together when either one appears.

this test alone is insufficient, as it often generates coarse-grained results -e.g., baseball glove, softball glove, and Hi-Def TV 2 .


Such vast corpora have led to leaps in the performance of many language-based tasks: the concept is that simple models trained on big data can outperform more complex models with fewer examples.

this new view comes with its own challenges: principally, how to effectively represent such large data sets so that model parameters can be efficiently extracted?


Count-Min sketch with conservative update (CM-CU): The QUERY procedure for CM-CU (Cormode, 2009;Goyal and Daumé III, 2011a) is identical to Count-Min.

to UPDATE an item "x" with frequency c, we first compute the frequencyĉ(x) of this item from the existing data structure ( ) and the counts are updated according to: The intuition is that, since the point query returns the minimum of all the d values, we update a counter only if it is necessary as indicated by ( * ).


From the above experiments, we conclude that tasks sensitive to under-estimation should use the CM-CU sketch, which guarantees over-estimation.

if we are willing to make some underestimation error with less over-estimation error, then LCU-WS and LCU-SWS are recommended.


Future work is to reduce the bit size of each counter (instead of the number of counters), as has been tried for other summaries (Talbot and Osborne, 2007;Talbot, 2009;Van Durme and Lall, 2009a) in NLP.

it may be challenging to combine this with conservative update.


Approximate sampling techniques have been developed over the last century and seem sufficient for most purposes.

the cases where one actually knows the quality of a sampling algorithm are very rare, and it is common practice to forget about the approximation and simply treat the result of a sampler as a set of i.i.d.


Accept x with probability p(x)/q(x), otherwise reject x; To obtain multiple samples, the algorithm is repeated several times.

for simple bounds, Figure 1: An example of an initial q-automaton (a), and the refined q-automaton (b) Each state corresponds to a context (only state 6 has a non-empty context) and each edge represents the emission of a symbol.


optimization) with such models, by decoupling the problem into two alternating steps that can each be handled by dynamic programming or other polynomial-time algorithms (Rush et al., 2010), an approach that has been applied to Statistical Machine Translation (phrase-based (Chang and Collins, 2011) and hierarchical (Rush and Collins, 2011)) among others.

sampling such distributions remains a difficult problem.


With two states (or one bit of annotation), our version of this parser gets 81.7 F1, edging out the comparable parser of Petrov and Klein (2008a).

our parser gets 83.2 with four states (two bits), short of the performance of prior work.


This approach worked largely because training was intractable: if the training algorithm could reach the global optimum, then this approach might have yielded no gain.

because the optimization technique is local, the same algorithm produced multiple grammars.


θ m will be particularly motivated to correct the latter, because they are less like the gold tree.

θ m will ignore the other "correct" segments, because q has already sufficiently captured them.


It scored 90.1/89.4 F1 on length 40 and all sentences respectively, slightly edging out the 90.0/89.3 F1 of Petrov and Klein (2008a).

it is not quite as good at exact match: 37.7/35.3 vs 40.1/37.7.


Language models are designed to assign probability to sentences.

approximate search algorithms use estimates for sentence fragments.


As alluded to in the introduction, the first few words of a sentence fragment are typically scored using lower-order entries from an N -gram language model.

kneser-Ney smoothing (kneser and Ney, 1995) conditions lower-order probabilities on backing off.


Developing good discourse-level models is difficult, and considering the modest translation quality that has long been achieved by SMT, there have been more pressing problems to solve and lower hanging fruit to pick.

we argue that the popular DP beam search algorithm, which delivers excellent decoding performance, but imposes a particular kind of local dependency structure on the feature models, has also had its share in driving researchers away from discourse-level problems.


While these representations are task-specific, they could be used across tasks in a multi-task learning setup.

in order to fairly compare to related work, we use only the supervised data of each task.


(2002) in requiring a representation in which two lexical items in an antonymy relation should lie at opposite ends of an axis.

in contrast to the logical axes used previously, we desire that antonyms should lie at the opposite ends of a sphere lying in a continuous and automatically induced vector space.


Given the context space model, we may use a linear regression or a k-nearest neighbors approach to embed out-of-thesaurus words into the thesaurusspace representation.

as near words in the context space may be synonyms in addition to other semantically related words (including antonyms), such approaches can potentially be noisy.


WordNet provides significantly greater coverage with approximately 227k synsets involving multiple words, and a vocabulary of about 190k words.

it is also much sparser, with 5.3 words per sense on average as opposed to 10.3 in the thesaurus, and has only 62,821 pairs of antonyms.


The Bloomsbury-based system is able to answer 153 questions, and the best dimension setting is 300, which answers 132 questions correctly and thus archives 0.863 in precision.

the larger vocabulary in WordNet helps the system answer 160 questions but the quality is not as good.


In contrast, the WordNet-based methods (Lines 1-3) attempted 936 questions.

consistent with what we observed on the development set, the WordNet-based model is inferior.


One of the most appealing aspects of so-called distributional semantic models (see Turney and Pantel (2010) for a recent overview) is that they afford some hope for a non-trivial, computationally tractable treatment of the context dependence of lexical meaning that might also approximate in interesting ways the psychological representation of that meaning (Andrews et al., 2009).

in order to have a complete theory of natural language meaning, these models must be supplied with or connected to a compositional semantics; otherwise, we will have no account of the recursive potential that natural language affords for the construction of novel complex contents.


If the first paragraph of a Wikipedia page contains the pronoun "she", but not "he", the article is considered to be about a female (and vice-versa).

when the page is assigned a non-person-related fine-grained NE type (e.g.


Therefore, when a similar-looking named entities appear in the same sentence, they are actually likely to refer to different entities.

in the sentence "Reggie Jackson, nicknamed Mr. October .


We design entity-based features so that the subsequent sieves "see" the decisions of the previous sieves and use entity-based features based on the intermediate clustering.

unlike (Raghunathan et al., 2010), we allow the subsequent sieves to change the decisions made by the lower sieves (since additional information becomes available).


We note that conceptually, the nested (B)+Predictions sieve should be identical to the baseline.

in practice, the surface form compatibility (SFC) features are generated for the nested sieve as well.


A Markov logic network consists of a set of first-order clauses (which we will refer to as formulas in the rest of the paper) just like in first-order logic.

different from first-order logic where a formula represents a hard constraint, in an MLN, these constraints are softened and they can be violated with some penalty.


In addition to research on resolution, there is also some work on effective annotation of abstract anaphora (Strube and Müller, 2003;Botley, 2006;Poesio and Artstein, 2008;Dipper and Zinsmeister, 2011).

to the best of our knowledge, there is currently no English corpus annotated for issue anaphora antecedents.


Taxonomies can serve as browsing tools for document collections.

given an arbitrary collection, pre-constructed taxonomies could not easily adapt to the specific topic/task present in the collection.


We filter out spams and advertisements and then search for more relevant Web documents to make the total number 1000.

not all topics can retrieve 1000 documents.


It is defined as: where P (c, v) = d∈Z P (d)P (c|d)P (v|d), C is the set of concepts in T , V is the set of nonstopwords in Z, and d is a document in Z. EMIM only evaluates the content of a browsing taxonomy, not its structure.

it is still popularly used to indicate how representative a browsing taxonomy is for a document collection.


Users that answered questions later in the question had higher accuracy.

there were users that were able to answer questions relatively early without sacrificing accuracy.


An alternative would be a more expressive action space (e.g., an action for every possible answer).

this conflates the question of when to buzz with what to answer.


This resembles approaches that merge different classifiers (Riedel et al., 2011) or attempt to estimate confidence of models (Blatz et al., 2004).


In this light, these methods are a complex form of instance bagging, and their development could be justified from this perspective.

given this justification, are improvements from MDL simply the result of standard ensemble learning effects, or are these methods really learning something about domain behavior?


Our empirical results suggest that MDL can be more effective in settings with domain-specific class biases.

we also saw differences in improvements for each method, and for different domains.


In this paper, we focus on incorporating the biases with HMM-type representations (Hidden Markov Model).

this technique can also be applied to other graphical model-based representations with little modification.


We suspect that the entropy feature, which is learned only from labeled sourcedomain data, makes the representation biased towards features that are important in the source domain only.

after we add in the distance bias and a parameter to balance the weights from both biases, the representation is able to capture the label information as well as the target domain features.


(2010) speculated that the observed advantage of Viterbi EM over standard EM is due to standard EM reserving too much probability mass to spurious parses in the E-step.

it is still unclear as to why Viterbi EM can avoid this problem.


However, entropy regularization is either motivated by the theoretical result that unlabeled data samples are informa-tive when classes are well separated (Grandvalet and Bengio, 2005), or derived from the expected conditional log-likelihood (Smith and Eisner, 2007).

our approach is motivated by the observed unambiguity of natural language grammars.


More and more English words are used in Chinese texts as names of organizations, products, terms and abbreviations, such as "eBay", "iPhone", "GDP", "Android" etc.

it is also a common phenomenon to use Chinese-English mixed texts in daily conversation, especially in communication among employers in large international corporations.


The mainstream method is to regard POS tagging as sequence labeling problems (Rabiner, 1990;Xue, 2003;Peng et al., 2004;Ng and Low, 2004).

the analysis of Chinese-English mixed texts is rarely involved in previous literature.


With a character-based perceptron as the core, combined with real-valued features such as language models, the cascaded model can efficiently utilize knowledge sources that are inconvenient to incorporate into the perceptron directly.

they use POS tags or word information in a Brute-Force way, which may suffer from the problem of time complexity.


English) and use parallel data to build a dictionary in the desired language and extend the dictionary coverage using label propagation.

parallel text does not exist for many pairs of languages and the proposed bilingual projection algorithms are fairly complex.


In these approaches, each observation corresponds to a particular word and each hidden state corresponds to a cluster.

using maximum likelihood training for such models does not achieve good results (Clark, 2003): maximum likelihood training tends to result in very ambiguous distributions for common words, in contradiction with the rather sparse word-tag distribution.


These two aspects are strongly intertwined: on the one hand, enabling language-independent text understanding would allow for the harvesting of more knowledge in arbitrary languages, while, on the other hand, bringing together the lexical and semantic information available in different languages would improve the quality of text understanding in arbitrary languages.

these two goals have hitherto never been achieved, as is attested to by the fact that research in a core language understanding task such as Word Sense Disambiguation (Navigli, 2009, WSD) has always been focused mostly on English.


All the above approaches to multilingual or crosslingual WSD rely on bilingual corpora, including those which exploit existing multilingual WordNetlike resources (Ide et al., 2002), or use automatically induced multilingual co-occurrence graphs (Silberer and Ponzetto, 2010).

this requirement is often very hard to satisfy, especially if we need wide coverage.


Formally, it computes the score for the j-th sense of w as follows: For instance, using the (normalized) sense distributions from our example, the ensemble distribution will be the following:  Computing a sense distribution for each translation using the same graph connectivity measure assumes that all translations are equal.

a leitmotif of multilingual WSD research is that translations restrict the set of candidate senses of the target word in the source language.


Domain-driven approaches have been shown to obtain the best performance among the unsupervised alternatives , especially when domain kernels are coupled with a syntagmatic one (Gliozzo et al., 2005).

their performance is typically lower than supervised systems.


The average number of glosses per term in our inventory is 1.9 (3.6 glosses on polysemous terms).

note that a monosemous word in our domain sense inventory does not necessarily make disambiguation easier, as i) we might have missed other domain-specific senses, ii) an uncovered, non-domain sense might fit a word occurrence (in this case, the domain WSD algorithms might be (wrongly) biased towards returning the only possible choice if a non-zero disambiguation score is calculated for it).


In sum, we can conclude that the higher correlation with human judgments indicates that integrating textual and perceptual modalities jointly is preferable to concatenation.

note that all models in Table 2 fall short of the human upper bound which we measured by calculating the reliability of Nelson et al.


On top of this cost, we need to alter the internal structure of the sentence-level models.

we can construct a dual decomposition algorithm which is efficient, produces a certificate when it finds an exact solution, and directly uses the sentence-level parsing models.


Graph features are defined over the factors of a graph-based dependency parser, which was shown to improve the accuracy of a transition-based parser by Zhang and Clark (2008).

while their features were limited to certain first-and second-order factors, we use features over second-and third-order factors as found in the parsers of Bohnet and Kuhn (2012).


In fact, this is the first example shown in Table 1, which is a noisy burst.

in Figure 2(b), the state sequence for the query "Nobel" is "0000011111," in which the longer and smoother burst corresponds to a true event.


In Figure 3, we can easily derive X 1 and X 2 have the same value 9 A simple evaluation method is that we label each one hour time slot as being part of a burst or not and compare with the gold standard.

in our experiments, we find that some methods tend to break one meaningful burst into small parts and easier to be affected by small fluctuations although they may have a good coverage of bursty points.


Another possible baseline is that we first merge all the activities, then apply the single-stream algorithm.

in our data set, we find that the number of activities in S t is significantly larger than that of the two types.


Their corpus consist of 1,400/450 posts written by 47 females and 24 males, respectively.

the ngram features were preselected based on whether they occurred with significant relative frequency in the language of one gender over the other.


We are not the first to predict gender from language features with online data.

to our knowledge, we are the first to contrast the two views, social and language-based, using online data and to question whether there is a clear understanding of what gender classifiers actually learn to predict from language.


While this seems like an interesting application of information theory for linguistic studies, it has also generated some controversies (Farmer et al., 2004).

our work departs from traditional scenarios significantly.


A simplistic approach might indeed involve comparing a test document to each training document.

in the winner-takes-all model described above, we can rely only on the result of comparing with the single best training document, which may not contain enough information to make a good prediction.


Nonetheless, using the centroid has the benefit of making a uniform grid less sensitive to cell size, such that larger cells can be used more reliably -especially important when there are few training documents.

when choosing representative locations for the leaves of a k-d tree, it is quite important to use the centroid because the leaves necessarily span the entire earth and none are discarded (since all have a roughly similar number of documents in them).


Operettas are a cultural phenomenon largely associated with France, Germany, and England and particularly with specific theaters in these countries.

other highly specific tokens such as KS01 have a very low average error because they occur in few documents and are thus highly unambiguous indicators of location.


Much of the writing styles recognized in rhetorical and composition theories involve deep syntactic elements in style (e.g., Bain (1887), Kemper (1987) Strunk and White (2008)).

previous research for automatic authorship attribution and computational stylometric analysis have relied mostly on shallow lexico-syntactic patterns (e.g., Mendenhall (1887), Mosteller and Wallace (1984), Stamatatos et al.


Some very recent works have shown that PCFG models can detect distributional difference in sentence structure in gender attribution (Sarawgi et al., 2011), authorship attribution (Raghavan et al., 2010), and native language identification (Wong and Dras, 2011).

still very little has been understood exactly what constitutes salient stylistic elements in sentence structures that characterize each author.


Among other corpora, a small subset (∼120K) of English portion of OntoNotes was used for this purpose.

the lack of a strong participation prevented the organizers from reaching any firm conclusions.


Since Arabic portion of the corpus is all newswire, this had no impact on it.

for both Chinese and Arabic, since we remove trace tokens corresponding to dropped pronouns, all the other layers of annotation had to be remapped to the remaining sequence of tree tokens.


This is somewhat expected as this is the second year for the English task, and so it does show a more mature and stable performance.

both Chinese and Arabic plots show much more divergence, with the Chinese and Arabic GB case showing the highest divergence.


For a given document, we have a forest of coreference trees, one tree for each coreferring cluster.

for the sake of simplicity, we link the root node of every coreference tree to an artificial root node, obtaining the document tree.


We use feature templates to generate such complex features.

we automatically generate templates using the entropy guided feature induction approach (Fernandes and Milidiú, 2012;Milidiú et al., 2008).


The development results are obtained with systems trained only on the training sets.

test set results are obtained by training on a larger dataset -the one obtained by concatenating training and development sets.


That is usually the case on NLP tasks, since golden values eliminate the additional noise introduced by automatic features.

during evaluation, we use the automatic values provided in the CoNLL shared task corpora.


We believe the reason for this is that these decoders are too similar and hence can not really benefit from each other.

when we used the AMP decoder as the first step, and a pair-wise decoder as the second, we saw an increase in performance, particularly with respect to the CEAFE metric.


Modifying the threshold of the AMP decoder gave very small differences in overall score and we kept the threshold for this decoder at 0.5.

when we increased the probability threshold for the second resolver, we found that performance increased across all languages.


For Chinese we also attempt to train a model for pronouns '你'(you) and '那'(that).

the results are not acceptable either since the features we select are not enough for the classifier.


The pronouns ' 这 '(this), ' 那 '(that), ' 这 里 '(here), ' 那 里 '(there) are not processed for we did not find a good solution.

in some cases the provided gender and number are not correct or missing and we had to label these mentions based on the appellation words of the training data.


The result is not as good as we supposed since the feature errors caused by these tools also made the coreferential errors.

a deeper error analysis is needed in the construction of deterministic rules.


From these figures, we can see that using feature selection in both initial feature sets, the performance improves.

the performance of our system is improved only on a few iteration.


The result seems reasonable because the model for testing use additional development data which is much smaller than training data.

the result on English test data seem a little odd.


Six systems participated in that task, UBIU (Zhekova and Kübler, 2010) among them.

since systems participated across the various languages rather irregularly, Recasens et al.


Following these works, we include a k nearest neighbor classifier for singleton mentions in UBIU with 19 commonly-used features described below.

unlike Ng (2004), we use a combination of the feature-and constraint-based approaches to incorporate the classifier's results.


A comparison of the results shows that there are only minor differences between them with gold outperforming auto apart from Arabic for which there is a drop of 3.75 points in the gold setting.

the small difference between all results shows that the quality of the automatic annotation is good enough for a CR system and that further improvements in the quality of the linguistic information will not necessarily improve CR.


This kind of model focuses on filtering with ordered tiers: One filter is applied at one time, from highest to lowest precision.

compared with learning approaches (Soon et al., 2001), since effective rules are quite heterogeneous in different languages, several filtering methods should be redesigned when different languages are considered.


In this case, we prefer to keep the candidate with a larger span.

we may predict "President Bush at Dayton" instead of "President Bush", if the parser incorrectly attaches the prepositional phrase.


The baseline system uses an identical model for coreference resolution on both pronouns and nonpronominal mentions.

in the literature (Bengtson and Roth, 2008;Rahman and Ng, 2011;Denis and Baldridge, 2007) the features for coreference resolution on pronouns and nonpronouns are usually different.


For example, lexical features play an important role in non-pronoun coreference resolution, but are less important for pronoun anaphora resolution.

gender features are not as important in non-pronoun coreference resolution.


It seems that the performance of the whole system is highly bottlenecked by that of the mention detection component.

it may not be true as the task requires removing singleton mentions that do not refer to any other mentions.


Thus more labeled instances can be collected within the fix budget.

the more useful and relevant but expensive instances from the target domain should also be queried at a certain rate.


One way to adapt it for document significance is to alter the numerator such that only the span-constrained bigram occurrences in -significant documents are considered in computing f (x, y).


Subsequent to model training, the methods uncover morph boundaries for new word forms by generating their most likely morph sequences according to the morph lexicons.

to learning morph lexicons (Poon et al., 2009;Kohonen et al., 2010), we study morphological segmentation by learning to directly predict morph boundaries based on their local substring contexts.


Intuitively, instead of the four class set {B, M, E, S}, a segmentation could be accomplished using only a set of two classes {B, M} as in (Green and DeNero, 2012).

similarly to Chinese word segmentation (Zhao et al., 2006), our preliminary experiments suggested that using the more fine-grained four class set {B, M, E, S} performed slightly better.


Nevertheless, for completeness, we computed the character accuracy for our Arabic data set, obtaining the accuracy 99.1%, which is close to their reported accuracy of 98.6%.

these values are not directly comparable due to our use of the Bible corpus by Snyder and Barzilay (2008) and their use of the Penn Arabic Treebank (Maamouri et al., 2004).


The result of McNemar test indicates that there is a significant difference (p < 0.01) between Semi-Boost and Semi-CRF.

there is no significant difference between Semi-Boost and Semi-PER.


When we trained models for ENER, Semi-PER consumed 32 GB and Semi-Boost consumed 33 GB.

semi-CRF could not train models because of the lack of memory.


This is because Semi-CRF maintains a weight vector and a parameter vector for L1-norm regularization and Semi-CRF considers all possible patterns generated from given sequences in training.

semi-PER and semi-Boost only consider features that appeared in correct ones and incorrectly recognized ones.


Models for sentence compression often compose text from units that are larger than individual tokens, such as n-grams which describe a token sequence or syntactic relations which comprise a dependency tree.

our approach is specifically motivated by the perspective that both these representations of a sentence-a sequence of tokens and a tree of dependency relations-are equally meaningful when considering its underlying fluency and integrity.


Similarly, consideration of dependency arcs allows the compressed dependency tree z to be scored using a rich set of indicator features over dependency labels, part-of-speech tags and even lexical features as in Filippova and Strube (2008).

unlike the bag-of-tokens scenario, these output structures cannot be constructed efficiently due to their interdependence.


n − 2, there can be no n-grams that feature the last n−r −1 tokens in the r'th position or the first n − r − 1 tokens in the (n − r + 1)'th position.

this is easily tackled computationally by assuming that the terminal n-gram replaces these missing n-grams for near-terminal tokens in constraint (6).


∀c ∈ {adj, dep} By itself, (8) would simply set all token indicators x i simultaneously to 0.

since START and ROOT have no incoming flow variables, the amount of commodity in the respective outgoing flow variables γ adj * j and γ dep * j remains unconstrained.


A vectorial representation of such pairs is the difference between the vectors representing the hypotheses in a pair.

this assumes that features are explicit and already available whereas we aim at automatically generating implicit patterns with kernel methods.


Vector-space word representations have been very successful in recent years at improving performance across a variety of NLP tasks.

common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled.


The objective function is then simply the sum of all individual costs over N training examples, plus a regularization term, which we try to minimize: The cimRNN model, though simple, is interesting to attest if morphemic semantics could be learned solely from an embedding.


Morfessor captures a general word structure of the form (pre * stm suf * ) + , which is handy for words in morphologically rich languages like Finnish or Turkish.

such general form is currently unnecessary in our models as the mor-phoRNNs assume input of the form pre * stm suf * for efficient learning of the RNN structures: a stem is always combined with an affix to yield a new stem.


For example, it returns V-ing as nearest neighbors for "commenting" and similarly, JJ-ness for "fearlessness", an unknown word that C&W cannot handle.

for those cases, the nearest neighbors are badly unrelated.


First, in ACs, the same-author posts can only interact via the confidence values assigned to them.

in our proposal, the same-author posts interact via Feature Definition SameDebate whether authors posted in same debate SameThread whether authors posted in same thread Replied whether one author replied to the other Table 5: Interaction features for the authoragreement classifier.


To answer this question, we apply both M 1 and M 2 to generate additional training instances, using a random selection of same-stance authors in place of M 2 's k-nearest neighbor method.

neither method yields an improvement in performance over the method on which it is based.


In Post 1, the author is anti-abortion, whereas in Post 4, the author is pro-abortion.

the first sentence in Post 1 gives a misleading clue about the author's stance, and so do the first two sentences in Post 4.


In this post, the author supports the legalization of marijuana.

the only useful hints about her stance are "marijuana should at least be decriminalized" and "weed can't kill you".


In Pn, a pro-Obama author explains why she thinks abortion is not wrong.

without the context from P1 that Obama is pro-abortion, it is not easy for a machine to classify Pn correctly.


Such databases are accurate, but as they broaden their scope they become increasingly incomplete.

to extending such a database, we present a system to query whether it contains an arbitrary fact.


For metrics which require a probability distribution, we pass the vectors through a sigmoid to obtain if we know both that (Greeks, are, mortal) and (men, are, mortal).

since the number of similar facts is likely to be small relative the number of candidate facts considered, this approach has the risk of losing the signal in the noise of uninformative candidates.


Coreference resolution systems can benefit greatly from inclusion of global context, and a number of recent approaches have demonstrated improvements when precomputing an alignment to external knowledge sources.

since alignment itself is a challenging task and is often noisy, existing systems either align conservatively, resulting in very few links, or combine the attributes of multiple candidates, leading to a conflation of entities.


The presence of spurious ambiguity causes this search space to be a directed graph rather than a tree, which considerably complicates the search, so spurious ambiguity was avoided whenever possible.

we claim that non-monotonicity and spurious ambiguity are not disadvantages in a modern statistical parsing system such as ours.


Instead, we want to learn a model that will offer its best prediction of Shift vs. Right-Arc, which we expect to usually be correct.

in those cases where the model does make the wrong decision, it should have the ability to later over-turn that decision, by having an unconstrained choice of Reduce vs. Left-Arc.


The MCMC based algorithms do not make any assumptions at all, and they can converge to the true posterior, either in joint or collapsed space as shown in Figure 1(b), 1(c).

one needs to have experience about the number of samples to be collected and the burn-in period.


The part of speech tagger has similar architecture to the one used for training the embeddings.

we have changed some of the network parameters, specifically, we use a hidden layer of size 300 and learning rate of 0.3.


Also, they have the advantage that they can be adapted to a specific task, as long as a large enough amount of parallel training data is available in order to adequately train the parameters of the Machine Translation system.

obtaining this task-specific training data by translating the original data by hand is very expensive and time-consuming.


Although these translators can generate many errors, they are an interesting way to obtain several hypotheses for a translation without much effort.

the use of these translators at testing time is not very convenient due to the fact that the system would depend on the Internet connection and the reaction time of the corresponding web pages.


It should be noted that by means of this process, the learned translator can represent and modelize the variability generated by the different translators.

due to the difficulty of the problem, this modelization may not be enough.


We think that separately processing the n-best translated sentences (for each input sentence) generated by the translator is not the best solution.

it would be better to adequately combine segments of different sentences.


It can be seen in Figures 3 and 4 that, for Configuration 2, when n takes the value 18, both error measures descend.

after this, the errors continue with their ascending tendency.


The use of pivot languages and wordalignment techniques over bilingual corpora has proved an effective approach for extracting paraphrases of words and short phrases.

inherent ambiguities in the pivot language(s) can lead to inadequate paraphrases.


Equation 1 allows paraphrases to be extracted by using multiple pivot languages such that these languages help discard inadequate paraphrases resulting from ambiguous pivot phrases.

in this formulation all senses of the input phrase are mixed together in a single distribution.


a noun) are paraphrased by words with other categories (e.g., a verb).

the approach does not solve the more complex issue of polysemous paraphrases: words with the same category but different meanings, such as the noun bank as financial institution and land alongside a river/lake.


It mixes valid senses of forma and (correctly) proposes the paraphrases manera and modo for sense (a), and tipo for sense (c).

paraphrases for sense (b) are over penalised and account for very little of the probability mass of the candidate paraphrases of forma.


Like in (Bannard and Callison-Burch, 2005), we use the Spanish WordNet 3 to bias our selection of phrases to paraphrase to contain ambiguous cases.

rather than biasing selection towards having more multi-word expressions, we chose to have more polysemous cases.


Note that CCB relies solely on the LM component to fit the paraphrase candidate to the context.

ccB-wsd and multi both have access to sense annotation, but while multi is able to benefit from multiple pivot languages, ccB-wsd can only pivot through the one English phrase provided as sense annotation.


On the one hand there is a drop in precision of about 9% for correctness with multi.

there is an improvement in recall: multi improves from 3% (top-1 guess) to 12% (top-3 guesses).


For example, under the simple model p(w f |w e ), the English word "free" may be translated into the Japanese word 自 由 (as in free speech) or 無 料 (as in free beer) with equal 0.5 probability; this low probability may cause both translation pairs to be rejected by the dictionary extraction algorithm.

given p(w f |w e , t), where t is "politics" or "shopping", we can allow high probabilities for both words depending on context.


We have seen how topic-dependent translation models p(w f |w e , t k ) is important in achieving good results.

eq.2 marginalizes over the topics so we do not know what topic-dependent lexicons are learned.


We address aspect detection at the mention level and our methods fall into the category of (unsupervised) lexicon-based approaches.

to supervised methods, lexicon-based approaches do not rely on labeled training data and thus scale better across domains 2 .


Popescu and Etzioni (2005) compare their results to the method by Hu and Liu (2004) and report significantly improved results.

their method relies on the private "Know-it-all" information extraction system and is therefore not suited as a baseline.


", according to the gold-standard annotations, 'system has' is corrected to 'systems have'.

this module keep the original word because of the 3rd person singular present verb, 'has'.


Besides, we found that applying the dependency criteria and moving window method in parallel leads to high recall but low precision.

the moving window method often fails because of insufficient evidence.


This can be detected and recovered by using a language model.

sVA is more complicated and it is more effective to determine the mistakes by using the linguistic and grammatical rules.


Note that there is spurious ambiguity in the model at two levels: Firstly, it is possible that there can be different derivations for the same tree-string pair.

during the application of the model this ambiguity occurs infrequently.


The heuristic pruning may undermine some of the advantages our model might have in taking whole sentence analyses into account to generate error corrections.

we find that despite this, the model is still able to generate hypothesis corrections that take non-local dependencies into consideration.


2011for correcting Japanese as a second language.

their training corpus comprised authentic learner sentences together with corrections made by native speakers on a social learning network website.


The original NUCLE corpus contains corrections for 27 error types.

the version used for the shared task only includes 5 error types and discards all the remaining corrections.


• Presence/value of the determiner in the noun group.

this is only a secondary cue, since it is not possible to determine if it is the determiner or the noun-number that is incorrect (e.g.


Absence of a determiner is indicated by a special class label NO DET.

since the number of determiners is large, a single multi-class classifier will result in ambiguity.


In English the verbs and their subjects have no fixed positions; in indicative sentences the verb most of the times (not immediately) but follows the subject, although not necessarily, e.g.

in sentences with expletives the subject follows the verb: there/EXPL are/VERB still many problems/SUBJ hampering engineering design process for innovations.


This should not be a big problem as the classifiers are called separately for their particular part-of-speech category (determiner, preposition, verb, or noun).

this puts a lot of weight on the part of speech tagger.


An example of this is the sentence: Take Singapore for example , these are installed... Take Singapore for example , surveillance is installed...

the replacement of these with surveillance is not in the task, so to get it correct, a system would have to hypothesize: Take Singapore for example , these is installed...


We achieved precision of 35.65%, recall of 16.56% and F 1 of 22.61% in the official score of our submitted result.

it was far from satisfactory mainly due to the ill settings of confidence parameters.


The subject of the verb show can be traced through R-A0 -> A0.

the performance of this part is partly correlated with the noun form that may have errors in the original text and the wrong SRL result brought about because of wrong sentence grammars.


Through experiments on a few sample ratios, we notice that feature selection using genetic algorithm is able to reduce the feature dimensionality to about 170,000 which greatly lowers down the downstream computational complexity.

the improvement contributed by GA after confidence tuning is not obvious as that before confidence tuning.


As the number of English learners is increasing world widely, the research topic of automated grammar error correction is lively discussed.

automated grammar error correction is a very difficult field and the result is not satisfactory.


To improve low recall of Han's method, to con-struct large training data is the best way.

it is very costly and hard work to obtain well edited error tagged corpus.


In several subfields of NLP, we have various evaluation metrics.

if a system A is reported to be better than a system B with respect to some metric M 1 , it need not be better with respect to some other metric M 2 .


This is similar to our proposed work in terms of transliteration and language modeling.

darwish (2013) does not target a conventionalized orthography, while our system targets COdA.


Additionally, there is some work on converting from dialectal Arabic to MSA, which is similar to our work in terms of processing a dialectal input.


This work is similar to ours in terms of text transliteration.


Similarly, an approach which treats the whole predicate-argument structure as an atomic unit (Regneri et al., 2010) will probably fail as well, as such a sparse model is unlikely to be effectively learnable even from large amounts of unlabeled data.

our embedding method would be expected to capture relevant features of the verb frames, namely, the transitive use for the predicate disembark and the effect of the particle away, and these features will then be used by the ranking component to make the correct prediction.


First of all, the SENNA embeddings tend to place antonyms / opposites near each other (e.g., come and go, or end and start).

'opposite' predicates appear in very different positions in scripts.


Surprisingly CJ08 rules produce as good results as BL, suggesting that maybe our learning set-ups are not that different.

an interesting question is in which situations using a more expressive model, EE, is beneficial.


This intuition is used by directional measures such as ClarkeDE, W eedsP rec and BalAP Inc.

we found that many features of the narrower term are often highly specific to that term and do not generalise even to hypernyms.


The reported MAP values are very low -this is due to many rare Word-Net hyponyms not occurring in the candidate set, for which all systems are automatically penalised.

this allows us to evaluate recall, making the results comparable between different systems and background datasets.


Lexicons are a simple yet powerful way to provide task-specific supervisory information to the model without the burden of labeling additional data.

while lexicons have proven useful in various NLP tasks, a small amount of noise in a lexicon can severely impair the its usefulness as a feature in log-linear models.


It may also be perceived as capturing an unsupervised knowledge representation schema, complementing supervised knowledge bases such as Freebase (Bollacker et al., 2008), as suggested by Riedel et al (2013).

language variability obstructs open IE from becoming a viable knowledge representation framework.


Also related, Riedel et al (2013) try to generalize over open IE extractions by combining knowledge from Freebase and globally predicting which unobserved propositions are true.

our work identifies inference relations between concrete pairs of observed propositions.


In a bootstrapped system, where the data is not fully labeled, existing systems score patterns by either ignoring the un-labeled entities or assuming them to be negative.

these scoring schemes cannot differentiate between patterns that extract good versus bad unlabeled entities.


Current pattern learning systems would score both patterns equally.

features like distributional similarity can predict 'cat' to be closer to {dog} than 'car', and a pattern learning system can use that information to rank 'Pattern 1' higher than 'Pattern 2'.


As discussed in the TAC 2011 pilot study by , there are situations that cannot be covered by this representation, such as recurring events, for ex-ample repeated marriages between two persons.

the most common situations for the relations covered in this task are captured correctly by this 4-tuple representation.


For example, from the text "they got married on Valentine's Day" a system can extract Valentine's Day as the surface form of the start of the per:spouse relation.

for a temporal scoping system it needs to normalize the temporal string to the date of February 14 and the year to which the document refers to explicitly in text or implicitly, such as the year in which the document was published.


To resolve them to actual dates/time is a non-trivial task.

the heuristic of employing the document's publication date as the reference works very well in practice e.g.


In addition, training can be performed on gold standard annotation.

model transfer assumes a common fea-ture representation across languages (McDonald et al., 2013), which can be a strong bottleneck.


The relation to annotation projection is obvious as both involve parallel data with one side being annotated.

the use of direct translation brings two important advantages.


In the following, we will compare results to our baseline as we have a comparable setup in those experiments.

most improvements shown below also apply in comparison with (McDonald et al., 2013).


Multi-source approaches are especially appealing using the translation approach.

initial experiments (which we omit in this presentation) revealed that simple concatenation is not sufficient to obtain results that improve upon the single-best translated treebanks.